Lane Detection and Object Detection

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 7 Issue 3, May-June 2023 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD57393 | Volume – 7 | Issue – 3 | May-June 2023 Page 365
Lane Detection and Object Detection
Harshada Annasaheb Nikam, Pranav Ashokrao Survase, Ketan Patole, Mansi Sanjay Gore
Department of Information Technology (IT) Engineering, Siddhant College of Engineering, Pune, Maharashtra, India
ABSTRACT
Every person in this world is concerned about being safe. Increasing
safety and reducing road accidents, thereby saving lives are one of
great interest in the context of Advanced Driver Assistance Systems.
Among the complex and challenging tasks of future road vehicles is
road lane detection or road boundaries detection. In driving
assistance systems, obstacle detection especially for moving object
detection is a key component of collision avoidance and also detect
signs boards. The most frequently used principal approach to detect
road boundaries and lanes using vision system on the vehicle. In
proposed system, we can implement of lane Object detection and also
detect Signs board with the help of AI and Deep learning Technique.
Input as Dataset to trained using CNN algorithm and creat beast
model. Using these model to classify or detect output.
How to cite this paper: Harshada
Annasaheb Nikam | Pranav Ashokrao
Survase | Ketan Patole | Mansi Sanjay
Gore "Lane Detection and Object
Detection"
Published in
International Journal
of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-7 |
Issue-3, June 2023, pp.365-381, URL:
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Scientific Research and Development
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1. INTRODUCTION
Everyone on the planet is concerned about their
safety. People who travel from one location to
another hope to arrive safely. Without any unexpected
events, such as traffic accidents, which may occur
while travelling. Using improved driving assistances,
we can avoid road accidents. In most populated
countries, vehicle collisions are still the major cause
of accidental death and injury.
People are investing a lot of money on advancements
in driving tactics that ensure safety in order to boost
safety and reduce road accidents. Technology
encourages people to think more about how to
improve safety and save lives. Road lanes detection
or boundaries detection (white and black lines on
roads) and Obstacles detection (cars, pedestrians,
trees, etc) are complex and challenging tasks for
future road vehicles, especially for moving object
detection and detecting road signs like (Speed, Right,
Left signs) is a key component of collision avoidance
in driving assistance systems.
The AI technology uses a camera through capture the
front view, then utilizes a CNN algorithms to detect
lanes, objects, and road signs. To detect the lanes,
objects, and road signs, a diverse methodology is
applied. Vehicles equipped with sophisticated
systems such as road lane detection and obstacle
detection are safer, which is critical in reducing the
number of people killed or injured in car accidents.
The use of a vehicle’s visual system is one of the
most common methods of detection.
1.1. MOTIVATION
Safety-critical computer vision-based on-road
systems demand excellent real-time performance, and
these systems must work consistently in a variety of
road, weather, and traffic circumstances. The
availability of a large variety of advanced
technologies in software component integration
provides a number of unique issues in the design and
developments of computer vision-based improved
driver assistance systems for intelligent vehicles .A
wide selection of open source libraries, such as
OpenCV and Python, provide adequate motivation to
design, implement, and test road lane detection
Object detection, as well as sign board detection
methods.
1.2. PROBLEM DEFINITION
Lane object Detection Systems which work with the
intention to recognize the lane borders and object,
IJTSRD57393

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
Sign Board on road and further prompts the driver if
he switches and moves to erroneous lane markings.
To detect object on road (like cars, peoples, animals)
with the help of computer vision technology.
2. LITERATURE SURVEY
2.1. STUDY OF RESEARCH PAPER
Hui Lv, Chao Liu, Xiaowei Zhao, Chunyan Xu, Zhen
Cui, Jian Yang [1]., we proposed by a present a deep
neural network based real-time integrated framework
to detect objects, lane markings, and drivable space
using a monocular camera for advanced driver
assistance systems. The object detection framework
detects and tracks objects on the road such as cars,
trucks, pedestrians, bicycles, motorcycles, and traffic
signs. The lane detection framework identifies the
different lane markings on the road and also
distinguishes between the ego lane and adjacent lane
boundaries.
Mehdi FENICHE, Tomader MAZRI [2] are ex
planing that Nowadays, Lane detection and tracking
modules are considered as central requirements in
every Intelligent Transportation System (ITS)
development. The extracted lane information could be
used in several smart applications for lane keeping
systems, lane departure warning and avoiding
collisions with other vehicles. In this proposed work,
we are presenting, reviewing and comparing the
different vision based algorithms used for detecting
road lanes in autonomous vehicles.
Jung Uk Kim and Yong Man Ro [3] author proposed
a system, Object detection became one of the major
fields in computer vision. In object detection, object
classification and object localization tasks are
conducted. Previous deep learning based object
detection networks perform with feature maps
generated by completely shared networks. However,
object classification focuses on the most
discriminative object part of the feature map.
Whereas, object localization requires a feature map
that is focused on the entire area of the object.
Ziqiang Sun [4], are evaluating Lane detection is a
key reference for traffic safety .In the intelligent
transportation system, lane line is the most important
traffic sign in road traffic, which can restrain and
guarantee the running of vehicles, so as to maintain
the safety highly secured [1-2]. Machine vision is
considered to be effective and simple during the
process of lane detection. Lane line detection and
identification has become a basic and necessary
functional module in the field of vehicle safety and
intelligent vehicle navigation, which can not only
reduce the occurrence of traffic accidents, but also
provide help for in depth research on intelligent
traffic. The lane detection technology based on
machine vision also has the advantages of low cost
and strong versatility and has been widely used.
3. SOFTWARE REQUIREMENTS
SPECIFICATION
3.1. PROJECT SCOPE
lane and object is a part of a road that is designed for
a single line of vehicles and is used to identify an
object and then road signs. It is used to guide and
control drivers, as well as to reduce traffic congestion.
As the incidence of traffic accidents rises, so does
concern about the nature of the accidents. Human
mistake is the most common cause. As a result, lane
and object detection systems, as well as sign board
detection systems, are being developed as a means of
supporting the driver
3.2. ASSUMPTIONS AND DEPENDENCIES
Assumptions:
We have used Python Technique. Input as lane Object
and road sign images dataset. Dependencies:
We have used python libraries like Tensorflow, keras,
opencv and Tkinter. Output to detect Lane Object and
also Detect road sign.
3.3. FUNCTIONAL REQUIREMENTS
Functional Specification:
The application is user friendly.
It provides an easy interface to user.
The accessibility or response time of the
application should be fast.
Dependency and Constraints:
End User application will be developed in
Windows OS.
All scripts shall be written in Python.
Application design pattern shall be Singleton.
3.4. NON FUNCTIONAL REQUIREMENT
3.4.1. Performance Requirements
The performance of the functions and every module
must be well. The overall performance of the
software will enable the users to work recently.
Performance of encryption of data should be fast.
Performance of the providing virtual environment
should be fast Safety Requirement
The application is designed in modules where errors
can be detected and xedeasily.
This makes it easier to install and update new
functionality if required.
3.4.2. Safety Requirement
The application is designed in modules where errors
can be detected and fixed easily.
This makes it easier to install and update new
functionality if required.

3.4.3. Software Quality Attributes
Our software has many quality attribute that are given
below:-
Adaptability: This software is adaptable by all users.
Availability: This software is freely available to all
users. The availability of the software is easy for
everyone.
Maintainability: After the deployment of the project if
any error occurs then it can be easily maintained by
the software developer.
Reliability: The performance of the software is better
which will increase the reliability of the Software.
User Friendliness: Since, the software is a GUI
application; the output generated is much user
friendly in its behavior.
Integrity: Integrity refers to the extent to which access
to software or data by unauthorized persons can be
controlled.
Security: Users are authenticated using many security
phases so reliable security is provided.
Testability: The software will be tested considering
all the aspects.
3.5. SYSTEM REQUIREMENT
3.5.1. Hardware Interfaces:
Hardware : intel core
Speed : 2.80 GHz
RAM : 8GB
HardDisk : 500 GB
Key Board: Standard Windows Keyboard
3.5.2. Software Interfaces
Operating System: Windows 10(64 Bit)
IDE: Spyder
Programming Language : python version 3.7,3.8
Libraries: Tensor Flow, OpenCv, Keras,Numpy
3.6. SYSTEM IMPLEMENTATION PLAN
The System Implementation plan table, shows the overall schedule of tasks compilation and time duration
required for each task.
Sr. No. Name/Title Start Date End Date
1 Preliminary Survey
2 Introduction and Problem Statement
3 Literature Survey
4 Project Statement
5 Software Requirement And Specification
6 System Design
7 Partial Report Submission
8 Architecture Design
9 Implementation
10 Deployement
11 Testing
12 Paper Publish
13 Report Submission

4. SYSTEM DESIGN
4.1. SYSTEM ARCHITECTURE
Figure 4.1: system Architecture
4.1.1. Data Flow Diagram
In Data Flow Diagram, we Show that flow of data in our system in DFD0 we show that base DFD in which
rectangle present input as well as output and circle show our system, In DFD1 we show actual input and actual
output of system input of our system is text or image and output is rumor detected
Figure 4.2: Data Flow diagram
Figure 4.3: Data Flow diagram
4.2. UML DIAGRAMS
Unified Modeling Language is a standard language for writing software blueprints. The UML may be used to
visualize, specify, construct and document the artifacts of a software intensive system. UML is process
independent, although optimally it should be used in process that is use case driven, architecture-centric,
iterative, and incremental. The Number of UML Diagram is available.
Activity Diagram.
Sequence Diagram.
Use case Diagram.

Activity Diagram
Figure 4.4: Activity Diagram
Sequence Diagram
Figure 4.5: Sequence Diagram

Use case Diagram
Figure 4.6: Use case Diagram
4.2.1. Analysis Models: Waterfall Model
SDLC Models stands for Software Development Life Cycle Models. In this article, we explore the most widely
used SDLC methodologies such as Agile. Each software development life cycle model starts with the analysis,
in which the Also, here are defined the technologies used in the project, team load. One of the basic notions of
the software development process is SDLC models which stands for Software Development Life Cycle models.
SDLC – is a continuous process, which starts from the moment, when it’s made a decision to launch the project,
and it ends at the moment of its full remove from the exploitation. There is no one single SDLC model. They are
divided into main groups, each with its features and weaknesses.

Figure 4.7: Waterfall Model
1. Requirement Analysis - Requirement Analysis is the most important and necessary stage in SDLC. The
senior members of the team perform it with inputs from all the stakeholders and domain experts or SMEs in
the industry. Planning for the quality assurance requirements and identifications of the risks associated with
the projects is also done at this stage. Business analyst and Project organizer set up a meeting with the client
to gather all the data like what the customer wants to build, who will be the end user, what is the objective of
the product. Before creating a product, a core understanding or knowledge of the product is very necessary.
2. System Design - The next phase is about to bring down all the knowledge of requirements, analysis, and
design of the software project. This phase is the product of the last two, like inputs from the customer and
requirement gathering.
3. Implementation - In this phase of SDLC, the actual development begins, and the programming is built. The
implementation of design begins concerning writing code. Developers have to follow the coding guidelines
described by their management and programming tools like compilers, interpreters, debuggers, etc. are used
to develop and implement the code.
4. Testing - After the code is generated, it is tested against the requirements to make sure that the products are
solving the needs addressed and gathered during the requirements stage. During this stage, unit testing,
integration testing, system testing, acceptance testing are done.
5. Deployment - Once the software is certified, and no bugs or errors are stated, then it is deployed. Then based
on the assessment, the software may be released as it is or with suggested enhancement in the object
segment. After the software is deployed, then its maintenance begins.
6. Maintenance - Once when the client starts using the developed systems, then the real issues come up and
requirements to be solved from time to time. This procedure where the care is taken for the developed
product is known as maintenance.
5. Project Plan
5.1. PROJECT ESTIMATES
5.1.1. Reconciled Estimates
Cost Estimate The project cost can be found using any one of the model. COCOMO 1 Model COCOMO-2
Model Model -1: The basic COCOMO model computes software development efforts as a function of program

size expressed in estimated lines of code. Model-2: The intermediate COCOMO model computes software
development efforts as a function of program size and a set of cost drivers that include subjective assessment of
the product, hardware, personnel, project attributes Model-3: The advanced COCOMO model incorporates all
characteristics of the intermediate version with an assessment of the cost drivers impact on each step of the
software engineering process. Following is the basic COCOMO -2 model.
1. Software Project A(b) B(b) C(b) D(b)
2. Organic 2.4 1.05 2.5 0.38
3. Semi-detached 3.0 1.22 2.5 0.35
4. Embedded 3.6 1.20 2.5 0.32 25
The basic COCOMO -2 model equations take form
E=A(b)KLOCB(b)
D=C(b)ED(b)
Where E is the effort applied in person months. D is development time in chronological month. KLOC is
estimated number of delivered lines of code for the project. This project can be classified as Semidetached
software project. The rough estimate of number of lines of this project is 9.072k.Applying the above formula
E=3.0*(9.072)1.22
5.1.2. Project Resources
Well configured Laptop, Spyder software, 2 GHZ CPU speed, 2 GB RAM, Internet connection.
5.2. RISK MANAGEMENT
5.2.1. Risk Identification
1. Have top software and customer managers formally committed to support the project?
Ans-Not applicable.
2. Are end-users enthusiastically committed to the project and the system/product tobe built?
Ans-Not known at this time.
3. Are requirements fully understood by the software engineering team and its customers?
Ans-Yes
4. Have customers been involved fully in the definition of requirements?
Ans-Not applicable
5. Do end-users have realistic expectations?
Ans-Not applicable
6. Does the software engineering team have the right mix of skills?
Ans-yes
7. Are project requirements stable?
Ans-Not applicable
5.2.2. Risk Analysis
Figure 5.1: Risk Table
Figure 5.2: Risk Probability definitions?

Figure 5.3: Risk Impact definitions?
5.2.3. Risk Mitigation Risk Monitoring and Risk Management
1. Risk Mitigation:
If a software team adopts a proactive approach to risk, avoidance is always the best strategy. This is achieved by
developing a plan for risk mitigation. To mitigate this risk, you would develop a strategy for reducing turnover.
Among the possible steps to be taken are:
Meet with current staff to determine causes for turnover (e.g., poor working condi-tions, low pay,
competitive job market).
Mitigate those causes that are under your control before the project starts.
Once the project commences, assume turnover will occur and develop techniquesto ensure continuity when
people leave.
Organize project teams so that information about each development activity iswidely dispersed.
Define work product standards and establish mechanisms to be sure that all modulesand documents are
developed in a timely manner.
Conduct peer reviews of all work.
Assign a backup staff member for every critical technologist.
2. Risk Monitoring
As the project proceeds, risk-monitoring activities commence. The project manager monitors factors that may
provide an indication of whether the risk is becoming more or less likely. In the case of high staff turnover, the
general attitude of team members based on project pressures, the degree to which the team has jelled,
interpersonal relationships among team members, potential problems with compensation and benefits, and the
availability of jobs within the company and outside it are all monitored.
3. Risk Management
Risk management and contingency planning assumes that mitigation efforts have failed and that the risk has
become a reality. Continuing the example, the project is well under way and a number of people announce that
they will be leaving. If the mitigation strategy has been followed, backup is available, information is
documented, and knowledge has been dispersed across the team. In addition, you can temporarily refocus
resources (and readjust the project schedule) to those functions that are fully staffed, enabling newcomers who
must be added to the team to “get up to speed.” Those individuals who are leaving are asked to stop all work and
spend their last weeks in “knowledge transfer mode.” This might include video based knowledge capture, the
development of “commentary documents or Wikis,” and/or meeting with other team members who will remain
on the project.
5.3. PROJECT SCHEDULE
5.3.1. Project Task Set
Major Tasks in the Project stages are:
Task 1: correctness
Task 2: availability
Task 3: integrity

5.3.2. Task Network
Figure 5.4: Task Network
5.3.3. Timeline Chart
Figure 5.5: Timeline Chart
5.4. TEAM ORGANIZATION
5.4.1. Team Structure
The team structure for the project is identified. There are total 2 members in our team and roles are defined. All
members are contributing in all the phases of project.
Sr. No. Name/Title Start Date End Date
1 Preliminary Survey
2 Introduction and Problem Statement
3 Literature Survey
4 Project Statement
5 Software Requirement And Specification
6 System Design
7 Partial Report Submission
8 Architecture Design
9 Implementation
10 Deployement
11 Testing
12 Paper Publish
13 Report Submission
5.4.2. Management reporting and communication:
Well planning mechanisms are used for progress reporting and inter/intra team communication are identified as
per requirements of the project.

6. Project Implementation
6.1. OVERVIEW OF PROJECT MODULES
In this chapter we are going to have an overview about how much time does it took to complete each task like-
Preliminary Survey Introduction and Problem Statement, Literature Survey, Project Statement, Software
Requirement and Specification, System Design, Partial Report Submission, Architecture Design,
Implementation, Deployment, Testing, Paper Publish, Report Submission. This chapter also gives focus on
stakeholder list which gives information about project type, customer of the proposed system, user and project
member who developed the system.
6.2. TOOLS AND TECHNOLOGIES USED
Python is an interpreted, high-level and general-purpose programming language. Created by Guido van Rossum
and first released in 1991, Python’s design philosophy emphasizes code readability with its notable use of
significant white space. Its language constructs and object-oriented approach aim to help programmers write
clear, logical code for small and large-scale projects.
Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including
structured (particularly, procedural), object-oriented, and functional programming. Python is often described as a
”batteries included” language due to its comprehensive standard library.
Python was created in the late 1980s as a successor to the ABC language. Python 2.0, released in 2000,
introduced features like list comprehensions and a garbage collection system with reference counting.
Python 3.0, released in 2008, was a major revision of the language that is not completely backward-compatible,
and much Python 2 code does not run unmodified on Python 3.
The Python 2 language was officially discontinued in 2020 (first planned for 2015), and ”Python 2.7.18 is the
last Python 2.7 release and therefore the last Python 2 release.”[30] No more security patches or other
improvements will be released for it. With Python 2’s end-of-life, only Python 3.6.x and later are supported.
Python interpreters are available for many operating systems. A global communityof programmers develops and
maintains CPython, a free and open-source reference implementation. A non-profit organization, the Python
Software Foundation, manages and directs resources for Python and CPython development.
Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde Informatica (CWI) in the
Netherlands as a successor to the ABC language (itself inspired by SETL), capable of exception handling and
interfacing with the Amoeba operating system. Its implementation began in December 1989. Van Rossum
shouldered sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his
”permanent vacation” from his responsibilities as Python’s Benevolent Dictator For Life, a title the Python
community bestowed upon him to reflect his long-term commitment as the project’s chief decision-maker. He
now shares his leadership as a member of a five-person steering council. In January 2019, active Python core
developers elected Brett Cannon, Nick Coghlan, Barry Warsaw, Carol Willing and Van Rossum to a five-
member ”Steering Council” to lead the project.
Anaconda: Anaconda is a free and open-source distribution of the Python and R programming languages for
scientific computing (data science, machine learning applications, large-scale data processing, predictive
analytics, etc.), that aims to simplify package management and deployment. The distribution includes data-
science packages suitable for Windows, Linux, and macOS. It is developed and maintained by Anaconda, Inc.,
which was founded by Peter Wang and Travis Oliphant in 2012. As an Anaconda, Inc. product, it is also known
as Anaconda Distribution or Anaconda Individual Edition, while other products from the company are Anaconda
Team Edition and Anaconda Enterprise Edition, both of which are not free.
Package versions in Anaconda are managed by the package management system conda. This package manager
was spun out as a separate open-source package as it ended up being useful on its own and for other things than
Python. There is also a small, bootstrap version of Anaconda called Miniconda, which includes only conda,
Python, the packages they depend on, and a small number of other packages. Anaconda distribution comes with
over 250 packages automatically installed, and over 7,500 additional open-source packages can be installed from
PyPI as well as the conda package and virtual environment manager. It also includes a GUI, Anaconda
Navigator, as a graphical alternative to the command line interface (CLI).
The big difference between conda and the pip package manager is in how package dependencies are managed,
which is a significant challenge for Python data science and the reason conda exists.

When pip installs a package, it automatically installs any dependent Python packages without checking if these
conflict with previously installed packages[citation needed]. It will install a package and any of its dependencies
regardless of the state of the existing installation[citation needed]. Because of this, a user with a working
installation of, for example, Google Tensorflow, can find that it stops working having used pip to install a
different package that requires a different version of the dependent numpy library than the one used by
Tensorflow. In some cases, the package may appear to work but produce different results in detail.
Spyder
Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists,
engineers and data analysts. It offers a unique combination of the advanced editing, analysis, debugging, and
profiling functionality of a comprehensive development tool with the data exploration, interactive execution,
deep inspection, and beautiful visualization capabilities of a scientific package.
Beyond its many built-in features, its abilities can be extended even further via its plugin system and API.
Furthermore, Spyder can also be used as a PyQt5 extension library, allowing you to build upon its functionality
and embed its components, such as the interactive console, in your own software.
Features
Editor
Work efficiently in a multi-language editor with a function/class browser, realtime code analysis tools (pyflakes,
pylint, and pycodestyle), automatic code completion (jedi and rope), horizontal/vertical splitting, and go-to-
definition.
Interactive console
Harness the power of as many I Python consoles as you like with full workspace and debugging support, all
within the flexibility of a full GUI interface. Instantly run your code by line, cell, or file, and render plots right in
line with the output or in interactive windows.
Documentation viewer
Render documentation in real-time with Sphinx for any class or function, whether external or user-created, from
either the Editor or a Console.
6.3. ALGORITHM
Convolutional Neural Network:
Convolutional neural network is one of the main categories to do image recognition, image classification, object
detection widely used.
CNN image classification takes the input image, process it and classify it. Computer sees an input image as array
of pixels depends on the image resolution.(h*w*h)
CNN is a widely-used image recognition model that has been shown to attain greater than 78.1 percent accuracy
on the Image Net dataset. The model is the conclusion of many ideas developed by many researchers over the
years. An 256x256x3 input representing a visual field of 256 pixels and 3 color (RGB) channels. Five
convolution layers, with a few interspersed max pooling operations. Successive stacks of “CNN Models”. A Soft
Max output layer at the end at an intermediate output layer just after the mixed layer. Steps involved in CNN are
Convolution layer in CNN is performed on an input image using a filter. Relu (Rectified Linear Unit) which
simply converts all of the negative values to 0 and keeps the positive values the same. Pooling layer is used to
reduce the spatial size of the Convolved Feature. They are of two types such as Max Pooling and Average
Pooling. Fully Connected layers in a neural networks is a layer where all the inputs from one layer are connected
to every activation unit of the next layer. These networks are commonly trained under a log loss (or cross-
entropy) system, giving a nonlinear variant of multinomial logistic regression.
2 CNN LAYERS:
Conv2D: It is the layer to convolve the image into multiple images activation is the activation function.
MaxPooling2D: It is used to max pool the value from the given size matrix and same is used for the next 2
layers.
Dropout: It is used to avoid over fitting on the dataset and dense is the output layer contains only one neuron
which decide to which category image belongs.

Fully Connected: It has neurons that are fully connected to the neurons in previous layer. This FC layer is often
kept as the final layer of a CNN with “SOFTMAX” as its activation function for multi-class classification
problems. The FC layer is responsible to predict the final class or label of the input image. Thus, it has an output
dimension of [1x1xN] where N denotes the number of classes or labels considered for classification.
Epochs: It tells us the number of times model will be trained in forward and backward pass.
Figure 6.1: CNN Algorithm
CNN’s were first developed and used around the 1980s. The most that a CNN could do at that time was
recognize handwritten digits. It was mostly used in the postal sectors to read zip codes, pin codes, etc. The
important thing to remember about any deep learning model is that it requires a large amount of data to train and
also requires a lot of computing resources. This was a major drawback for CNNs at that period and hence CNNs
were only limited to the postal sectors and it failed to enter the world of machine learning.
Convolutional neural network (CNN/ConvNet) is a class of deep neural networks, most commonly applied to
analyze visual imagery. Now when we think of a neural network we think about matrix multiplications but that
is not the case with ConvNet. It uses a special technique called Convolution. Now in mathematics convolution is
a mathematical operation on two functions that produces a third function that expresses how the shape of one is
modified by the other.
Input Layers: It’s the layer in which we give input to our model. The number of neurons in this layer is equal to
the total number of features in our data (number of pixels in the case of an image).
Hidden Layer: The input from the Input layer is then feed into the hidden layer. There can be many hidden layers
depending upon our model and data size. Each hidden layer can have different numbers of neurons which are
generally greater than the number of features. The output from each layer is computed by matrix multiplication
of output of the previous layer with learnable weights of that layer and then by the addition of learnable biases
followed by activation function which makes the network nonlinear.
Output Layer: The output from the hidden layer is then fed into a logistic function like sigmoid or softmax which
converts the output of each class into the probability score of each class.
7. Software Testing
Testing is an investigation conducted to provide stakeholders with information about the quality of the product
or service under test. Software testing also provides an objective, independent view of the software to allow the
business to appreciate and understand the risks of software implementation. Test techniques include, but are not
limited to, the process of executing a program or application with the intent of finding software bugs. Software
testing can also be stated as the process of validating and verifying that a software program or application or
product:
1. Meets the business and technical requirements that guided
2. Works as expected;
3. Can be implemented with the same characteristics
7.1. TYPES OF TESTING
7.1.1. Unit Testing
It focuses on smallest unit of software design. In this we test an individual unit or group of inter related units.

7.1.2. Regression Testing
The objective is to take unit tested components and build a program structure that has been dictated by design.
Integration testing is testing in which a group of components are combined to produce output.
7.1.3. Smoke Testing
Very time new module is added leads to changes in program. This type of testing make sure that whole
component works properly even after adding components to the complete program.
7.1.4. System Testing
In this software is tested such that it works fine for different operating system.It is covered under the black box
testing technique.
7.1.5. Test Cases

8. OTHER SPECIFICATION
8.1. ADVANTAGES
1. System To detect easily lane and object board.
2. Time Consuming and provide safety to driver.
3. Very help system to self-driver.
8.2. DISADVANTAGES
1. One disadvantage crash or damage camera then system is not working because system is also depend on
camera.
8.3. APPLICATION
1. River Assistance.
2. Lane Departure warning.
3. Lane-keeping systems.
4. Object analyze and detect.
9. Conclusion and Future Work
9.1. CONCLUSION
We have proposed a method for detecting and tracking multiple lanes Object by effectively fusing both frame-
by-frame lane detection and temporal dependencies over the sequence of frames, using a video taken by a single
dash-cam. And also detect sign board using CNN algorithm.
9.2. FUTURE WORK
1. The object recognition system can be applied in the area of surveillance system,face recognition,fault
detection, character recognition etc. The objective of this thesis is to develop an object recognition system to
recognize the 2D and 3D objects in the image. The performance of the object recognition system depends on
the features used and the classifier employed for recognition.
2. Future work will include vehicle departure warnings and pedestrian detection, orlane markings, which will
effectively help drivers improve driving safety
3. Bigger dataset can be used for executing this project in a more precise way. Instead of SVM mode, we can
use CNN for classification of traffic signs. There can be a better performance with high frame rate. Different
approaches like YOLO or SSD can also be used. YOLO (You Only Look Once) is a CNN algorithm that
uses single network for the input image, thus, creating bounding box and probability of the features across
image for detection purpose.

Annexure A
NP-Hard NP-Complete:
What is P?
P is set of all decision problems which can be solved in polynomial time by a deterministic.
Since it can be solved in polynomial time, it can be verified in polynomial time.
Therefore P is a subset of NP.
P: To identify road condition or road survey requires more man power, time and money. To resolve these
problems we need effective system.
What is NP?
NP means we can solve it in polynomial time if we can break the normal rules of step-by-step computing.
What is NP Hard?
A problem is NP-hard if an algorithm for solving it can be translated into one for solving any NP-problem
(nondeterministic polynomial time) problem. NP-hard therefore means ”at least as hard as any NP-problem,”
although it might, in fact, be harder.
Figure A.1: P Problem
NP-Hard:
Propose system analyze the road condition and road surface. It identify bad road patches and gives notification
to navigation system. For that we used inbuilt accelerometer sensor and gyroscope sensor. To improve the
system result we use decision tree algorithm. Propose system has self-managing database which collect data
from vehicle drivers android smart phones. This data update in real time periodically. Application utilizes this
data to inform other application users about road condition.
So here in this case the ‘P’ problem is NP hard.
i.e. P=NP-Hard
What is NP-Complete?
Since this amazing ”N” computer can also do anything a normal computer can, we know that ”P” problems
are also in ”NP”.
So, the easy problems are in ”P” (and ”NP”), but the really hard ones are *only* in ”NP”, and they are called
”NP-complete”.
Figure A.2: NP Problem

It is like saying there are things that People can do (”P”), there are things that Super People can do (”SP”),
and there are things *only* Super People can do (”SP-complete”).
Figure A.3: NP Complete Problem
NP-Complete:
We have used inbuilt mobile sensor to identify road conditions.
Hence the ‘P’ is NP-Complete in this case.
10. References
[1] “Machine vision-based coin separator and
counter” by Prashanna Rangan R, STM
Journals, STM Journals, Volume 5, Issue 1,
2018
[2] “Coin counting and sorting machine” by Prof.
Anupa Kavale, Prof.Shraddha Shukla, Prof.
Prachi Bramhe, 2019, 9th International
Conference on Emerging Trends in Engineering
and Technology - Signal and Information
Processing [ICETET-SIP-19]
[3] “Coin detection and recognition using neural
networks” by S. Mohamed mansoor roomi, R.
B. Jayanthi rajee, 2015, International
Conference on Circuit, Power and Computing
Technologies[ICCPCT]
[4] “Study on Automatic sorting and counting
machine for coin” by Liai Pan, and Qiulei Du,
vols 427 - 429 (2013) pp 872-875 (2013), Trans
Tech Publications, Switzerland
[5] “Automatic South African Coin Recognition
through Visual Template Matching” by Tharish
Sooruth, MandlenKosi V. Gwetu, 2018, IEEE
[6] ”Indian Coin Recognition System of Image
Segmentation by Heuristic Approach and
Houch Transform (HT)” by C. M. Velu and P.
Vivekanandhan, Int. J. Open Problems
Compt.Math., Vol. 2, No.2, June 2009.
[7] ”Indian Coin Detection and Sorting using SIFT
Algorithm” by Rohan.S.Prabhu, Sahil.G.
Khorjuvekar, Akshat.G.Poi, Vaibhav Naik,
IJSTE-International Journal of Science
Technology Engineering, Volume 2, Issue 10,
April 2016.
[8] Harveen Kaur and Neetu Sharma, “Modified
coin identification using Neural Network”,
IJETAE 2014
[9] Saranya das. Y. M and R. Pugazhenthi,
“HarrisHessian Algorithm for coin
Apprehension”, IJARCET 2013
[10] Tushar N. Nimbhorkar and M. M. Bartere,
“Coin Recognition by using Artificial Neural
Network”, IJCITB 2013

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